Good Initial Solution Research Articles

Availability Allocation for Distributed Clusters of the Cloud Infrastructure

The cloud environment capitalizes on the built-in redundancy of the large number of servers that make them up by enabling high levels of availability and reliability for applications. In this paper, we address the availability problem of the heterogeneous distributed clusters in the cloud and propose an availability allocation model with the goal of maximizing the system reliability. The allocation model involves three kinds of services according to the coupling classification. And the availability analysis scheme is designed for each coupling type. Besides, the prior knowledge of all the server availability value is imported to simplify the service disposal and achieve an optimal solution in a short time. The experiments show that this algorithm finds a good initial solution for availability allocation problem.

Robust makespan minimisation in identical parallel machine scheduling problem with interval data

Parallel machine scheduling problems are commonly encountered in a wide variety of manufacturing environments and have been extensively studied. This paper addresses a makespan minimisation scheduling problem on identical parallel machines, in which the specific processing time of each job is uncertain, and its probability distribution is unknown because of limited information. In this case, the deterministic or stochastic scheduling model may be unsuitable. We propose a robust (min–max regret) scheduling model for identifying a robust schedule with minimal maximal deviation from the corresponding optimal schedule across all possible job-processing times (called scenarios). These scenarios are specified as closed intervals. To solve the robust scheduling problem, which is NP-hard, we first prove that a regret-maximising scenario for any schedule belongs to a finite set of extreme point scenarios. We then derive two exact algorithms to optimise this problem using a general iterative relaxation procedure. Moreover, a good initial solution (optimal schedule under a mid-point scenario) for the aforementioned algorithms is discussed. Several heuristics are developed to solve large-scale problems. Finally, computational experiments are conducted to evaluate the performance of the proposed methods.

Fast Scan-Chain Ordering for 3-D-IC Designs Under Through-Silicon-Via (TSV) Constraints

This brief addresses the problem of scan-chain ordering under a limited number of through-silicon vias (TSVs), and proposes a fast two-stage algorithm to compute a final order of scan flip-flops. To enable 3-D optimization, a greedy algorithm, multiple fragment heuristic, is modified and combined with a dynamic closest-pair data structure, FastPair, to derive a good initial solution in stage one. Stage two initiates two local refinement techniques, 3-D planarization and 3-D relaxation, to reduce the wire (and/or power) cost and to relax the number of TSVs in use to meet TSV constraints, respectively. Experimental results show that the proposed algorithm results in comparable performance (in terms of wire cost only, power cost only, and both wire-and-power cost) to a genetic-algorithm method but runs two-order faster, which makes it practical for TSV-constrained scan-chain ordering for 3-D-IC designs.

A robust block-chain based tabu search algorithm for the dynamic lot sizing problem with product returns and remanufacturing

This paper studies the dynamic lot sizing problem with product returns and remanufacturing (DLRR). Given demands and returns over a planning horizon, DLRR is to determine a production schedule of manufacturing new products and/or remanufacturing returns such that demand in each period is satisfied and the total cost (set-up cost plus holding cost of inventory) is minimized.Since DLRR with general cost functions for set-ups of manufacturing and remanufacturing is NP-hard, we develop a tabu search to produce high-quality solutions. To generate a good initial solution, we use a block-chain based method where the planning horizon is split into a chain of blocks. A block may contain either a string of manufacturing set-ups, a string of remanufacturing set-ups, or both. Given the cost of each block, an initial solution corresponding to a best combination of blocks is found by solving a shortest-path problem. Neighboring operators aim at shifting integer variables for manufacturing and remanufacturing set-ups.We evaluate our algorithm on 6480 benchmark problems and compare it with other available algorithms. Computational results demonstrate that our algorithm produces an optimal solution in 96.60% of benchmark problems, with an average deviation of 0.00082% from optimality and it is a state-of-the-art method for DLRR.

Metaheuristic approach for constructing functional test‐suites

Today, software systems are complex and have many possible configurations. A deficient software testing process often leads to unfortunate consequences, including data losses, large economic losses, security breaches, and even bodily harm. Thus, the problem of performing effective and economical testing is a key issue. Combinatorial testing is a method that can reduce cost and increase the effectiveness of software testing for many applications. It is based on constructing economical sized test-suites that provide coverage of the most prevalent configurations. Mixed covering arrays (MCAs) are combinatorial structures that can be used to represent these test-suites. MCAs are combinatorial objects represented as matrices having a test case per row. MCAs are small, in comparison to an exhaustive approach, and guarantee a level of interaction coverage among the parameters involved. This study presents a metaheuristic approach based on a simulated annealing (SA) algorithm for constructing MCAs. This algorithm incorporates several distinguishing features, including an efficient heuristic to generate good quality initial solutions, and a compound neighbourhood function that combines two carefully designed neighbourhood functions. The experimental design involved a benchmark reported in the literature and two real cases of software components. The experimental evidence showed that the SA algorithm equals or improves the obtained results by other approaches reported in the literature, and also finds the optimal solution in some of the solved cases.

A flow direction weighting scheme for facility layout in cellular manufacturing

PurposeThe purpose of this paper is to re‐examine the solution methodology to the facility grouping problem in group technology originally outlined by Mukhopadhyay et al. The aim of this re‐examination is to attempt to resolve some of the problems associated with the original solution methodology and to provide the system designer with an improved technique for the creation of an efficient initial system design solution. Cellular manufacture by its very nature requires work‐part transfer both within a machine cell and in some instances between machine cells. By utilising a design methodology which seeks to minimise work‐part transfer, these non‐value but cost adding tasks are kept to a minimum, thereby providing a faster throughput time and lower operational costs for companies who use cellular manufacturing systems in their production operations.Design/methodology/approachIn approach, the technique advocated parallels the possible alternative solution method suggested by John et al. to resolve the problem of edge ranking ties which exists in the original Mukhopadhyay et al. solution technique. Presented and discussed in this contribution is a revised flow direction weighting scheme (FDWS) which simplifies the solution input data requirements and overcomes a perceived disadvantage in the earlier scheme outlined by John et al. The methodology outlined in the paper is subsequently evaluated for its generic nature, applicability and effectiveness via the use of previously published synthetic production data, and the findings compared to the results determined by several alternative approaches to the problem.FindingsThe development and application of the FDWS to cellular manufacturing system design is outlined in the paper and the usefulness of the technique is subsequently examined and compared to the results found for several competing methods. The approach provides for the identification of improved solutions to the test problems examined in both of the earlier contributions by Mukhopadhyay et al., and John et al. In addition, the results show that irrespective of the assessment criteria used, the FDWS technique provides solutions that are comparable to or better than the alternative approaches examined.Originality/valueThe proposed FDWS technique further contributes to the existing knowledge base on cellular manufacturing system design. It can provide good initial solution guidance for manufacturing system designers and thereby reduce the time required to achieve an acceptable and cost efficient design for a cellular production system.

Scheduling Semiconductor Multihead Testers Using Metaheuristic Techniques Embedded with Lot-Specific and Configuration-Specific Information

In the semiconductor back-end manufacturing, the device test central processing unit (CPU) is most costly and is typically the bottleneck machine at the test plant. A multihead tester contains a CPU and several test heads, each of which can be connected to a handler that processes one lot of the same device. The residence time of a lot is closely related to the product mix on test heads, which increases the complexity of this problem. It is critical for the test scheduling problem to reduce CPU's idle time and to increase tester utilization. In this paper, a multihead tester scheduling problem is formulated as an identical parallel machine scheduling problem with the objective of minimizing makespan. A heuristic grouping method is developed to obtain a good initial solution in a short time. Three metaheuristic techniques, using lot-specific and configuration-specific information, are proposed to receive a near-optimum and are compared to traditional approaches. Computational experiments show that a tabu search with lot-specific information outperforms all other competing approaches.

GENVNS-TS-CL-PR: A heuristic approach for solving the vehicle routing problem with simultaneous pickup and delivery

This work addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). Due to its complexity, we propose a heuristic algorithm for solving it, so-called GENVNS-TS-CL-PR. This algorithm combines the heuristic procedures Cheapest Insertion, Cheapest Insertion with multiple routes, GENIUS, Variable Neighborhood Search (VNS), Variable Neighborhood Descent (VND), Tabu Search (TS) and Path Relinking (PR). The first three procedures aim to obtain an good initial solution, and the VND and TS are used as local search methods for VNS. TS is called after some iterations without any improvement through of the VND. The PR procedure is called after each VNS iteration and it connects a local optimum with an elite solution generated during the search. The algorithm uses an strategy based on Candidate List to reduce the number of solutions evaluated in the solution space. The algorithm was tested on benchmark instances taken from the literature and it was able to generate high quality solutions.

PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem

Concerns over environmental degradation, legislative requirements and growing needs of business have fueled the growth of Closed Loop Supply Chains (CLSC). We consider a CLSC and address the issues of designing the network and of optimizing the distribution. Four variants of the problem are considered. The problem is modeled as an Integer Linear Program (ILP). We develop a constructive heuristic based on Vogel's approximation method–total opportunity cost method to provide good initial solutions to a priority-based simulated annealing heuristic, to accelerate its convergence. Trials on a set of hypothetical datasets have yielded encouraging results. The methodology is also tested using a case study data of a company producing electronic products. Implications for sustainability are discussed.

Scheduling physiotherapy treatment in an inpatient setting

Whilst many areas of healthcare have benefitted from purpose-built automated scheduling programs (e.g. nursing rosters, doctor’s surgeries, and operating theatres), physiotherapy has been somewhat left behind. This is a matter of concern, since inpatient physiotherapy is an important part of many treatment programmes in general hospitals and specialist rehabilitation units. This study seeks to help address this deficiency by detailing a computer program that has been introduced to timetable physiotherapy treatment at a major neurological rehabilitation unit in Cardiff, UK.The aim is to show how automated scheduling can provide a far more effective and efficient alternative to manual scheduling by hand. Whilst the scheduling procedure will undoubtedly differ from unit to unit, this paper outlines what are thought to be some of the most important objectives and constraints in addition to an adaptable methodology that can be employed to arrive at a good-quality solution in a timely fashion. The scheduling problem considered here is formulated as a multi-objective combinatorial optimisation problem due to the many competing objectives and constraints and the significant size of the solution space. An approximate solution is sought by means of a three stage local search based approach. The first stage is to construct a valid and good quality initial solution. The second stage is to improve this with respect to some of the more important soft constraints by applying a series of steepest descent based algorithms. Like hard constraints, these ’moderate’ constraints represent a discrete number of violations rather than contributions to an objective function. This allows for a more targeted approach in their solution. The third stage is then the optimisation of the (remaining) soft constraints using simulated annealing and tabu search.Results are favourable with typical timetable quality (as measured by constraint violations), being considerably better than under the former ’by hand’ approach. Since its introduction, employee time expended on timetable creation has been cut from eight hours per week to fewer than two, freeing up therapist time for clinical work.

Interactive Polyhedral Outer Approximation (IPOA) strategy for general multiobjective optimization problems

We propose an interactive polyhedral outer approximation (IPOA) method to solve a broad class of multiobjective optimization problems (MOP) with, possibly, nonlinear and nondifferentiable objective and constraint functions, and with continuous or discrete decision variables. During the interactive optimization phase, the method progressively constructs a polyhedral approximation of the decision-maker’s (DM’s) unknown preference structure and a polyhedral outer-approximation of the feasible set of MOP. The piecewise linear approximation of the DM’s preferences also provides a mechanism for testing the consistency of the DM’s assessments and removing inconsistencies; it also allows post-optimality analysis. All the feasible trial solutions are non-dominated (efficient, or Pareto-optimal) so preference assessments are made in the context of non-dominated alternatives only. Upper and lower bounds on the yet unknown optimal value are produced at every iteration, allowing terminating the search prematurely at a good-enough solution and providing information about the closeness of this solution to the optimal solution. The IPOA method includes a preliminary phase in which a limited probe of the efficient set is conducted in order to find a good initial trial solution for the interactive phase. The computational requirements of the algorithm are relatively simple. The results of an extensive computational study are reported.

Optimising container storage processes at multimodal terminals

Multimodal Container Terminals (MMCT) require comprehensive planning, programming and control in order to operate an efficient storage system. In this paper, a model has been developed for the storage system of the MMCT. It is an extension of the Blocks Relocation Problem, with incoming as well as outgoing containers, or ‘blocks’. The model deals with assigning containers to positions within the storage area, rehandling of containers to be retrieved, and calculation of the processing times in order to perform these actions. A number of constructive heuristics are presented in order to produce good initial solutions for this problem. Meta-heuristics are also used to improve on these solutions. Results from the various heuristics on a few case study problems are compared and discussed. This research will also provide a core piece of technology for the development of autonomous container handling systems for terminals.

Branch and bound for the cutwidth minimization problem

The cutwidth minimization problem consists of finding a linear arrangement of the vertices of a graph where the maximum number of cuts between the edges of the graph and a line separating consecutive vertices is minimized. We first review previous approaches for special classes of graphs, followed by lower bounds and then a linear integer formulation for the general problem. We then propose a branch-and-bound algorithm based on different lower bounds on the cutwidth of partial solutions. Additionally, we introduce a Greedy Randomized Adaptive Search Procedure (GRASP) heuristic to obtain good initial solutions. The combination of the branch-and-bound and GRASP methods results in optimal solutions or a reduced relative gap (difference between upper and lower bounds) on the instances tested. Empirical results with a collection of previously reported instances indicate that the proposed algorithm is able to solve all the small instances (up to 32 vertices) as well as some of the large instances tested (up to 158 vertices) using less than 30 minutes of CPU time. We compare the results of our method with previous lower bounds, and with the best previous linear integer formulation solved using Cplex. Both comparisons favor the proposed procedure.

Model-based structural optimization of seawater desalination plants

Membrane-based processes and thermal desalination plants are two alternatives to overcome the problem of fresh water supply by means of seawater desalination. In this work the combination of two types of desalination technologies, a reverse osmosis network (RON) and a forward-feed multi-effect distillation (FF-MED), in a hybrid plant is considered as a structural optimization problem. Deterministic mixed-integer non-linear programming techniques (MINLP) are used to rigorously optimize operational parameters and process structure simultaneously. A generalized superstructure for the hybrid desalination plant is constructed and conceptual design considerations are used to reduce its complexity. A mathematical model for the whole plant and an economical objective function are provided. The resulting model is highly nonlinear and non-convex. A stepwise optimization strategy is proposed, including a specialized initialization scheme to robustly compute good initial solutions with a minimum of user interaction. Results for various specifications illustrate the application of the proposed optimization model. The performance of the suggested method is compared to that obtained with the global solver BARON.

Fast heuristic for constrained homogenous T-shape cutting patterns

Homogenous T-shape (HTS) cutting patterns are welcomed when the two-phase process is used to produce rectangular pieces from the stock plate, where the plate is cut into homogenous strips at the first phase, and the strips are divided into pieces at the second phase. A heuristic is presented for generating constrained HTS patterns, where the objective is to maximize the pattern value that is equal to the total value of the included pieces, observing the upper bound constraint on the frequency of each piece type. The heuristic is based on dynamic programming and branch-and-bound techniques. It can yield solutions close to optimal with short computation time. By providing good initial solutions, the heuristic can greatly improve the time efficiency of an existing exact branch-and-bound algorithm.

Efficient clustering and simulated annealing approach for circuit partitioning

Circuit net list bipartitioning using simulated annealing technique has been proposed in the paper. The method converges asymptotically and probabilistically to global optimization. The circuit net list is partitioned into two partitions such that the number of interconnections between the partitions is minimized. The proposed method begins with an innovative clustering technique to obtain a good initial solution. Results obtained show the versatility of the proposed method in solving non polynomial hard problems of circuit net list partitioning and show an improvement over those available in literature.

웹 개인화를 위한 웹사용자 클러스터링 알고리즘에 관한 연구

The user clustering for web navigation pattern discovery is very useful to get preference and behavior pattern of users for web pages. In addition, the information by the user clustering is very essential for web personalization or customer grouping. In this paper, an algorithm for clustering the web navigation path of users is proposed and then some special navigation patterns can be recognized by the algorithm. The proposed algorithm has two clustering phases. In the first phase, all paths are classified into k-groups on the bases of the their similarities. The initial solution obtained in the first phase is not global optimum but it gives a good and feasible initial solution for the second phase. In the second phase, the first phase solution is improved by revising the k-means algorithm. In the revised K-means algorithm, grouping the paths is performed by the hyperplane instead of the distance between a path and a group center. Experimental results show that the proposed method is more efficient.

A GA/TS algorithm for the stage shop scheduling problem

This paper presents a special case of the general shop called stage shop problem. The stage shop is a more realistic generalization of the mixed shop problem. In the stage shop problem, each job has several stages of operations. In order to solve the stage shop problem with makespan objective function, an existing neighborhood of job shop is used. In this neighborhood, few enhanced conditions are proposed to prevent cycle generation. In addition, a new neighborhood for operations that belong to the same job is presented. These neighborhoods are applied to the stage shop problem in a tabu search framework. A genetic algorithm is used to obtain good initial solutions. An existing lower bound of the job shop is adapted to our problem and the computational results have been compared to it. Our algorithm has reached the optimal solutions for more than half of the problem instances.

An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations

Cell formation is the first step in the design of cellular manufacturing systems. In this study, an efficient tabu search algorithm based on a similarity coefficient is proposed to solve the cell formation problem with alternative process routings and machine reliability considerations. In the proposed algorithm, good initial solutions are first generated and later on improved by a tabu search algorithm combining the mutation operator and an effective neighborhood solution searching mechanism. Computational experiences from test problems show that the proposed approach is extremely effective and efficient. When compared with the mathematical programming approach which took three hours to solve problems, the proposed algorithm is able to produce optimal solutions in less than 2 s.

The global solver in the LINDO API

The global solver in the LINDO Application Programming Interface (LINDO API) finds guaranteed global optima to nonconvex, nonlinear and integer mathematical models using the branch and bound/relax approach. We describe (a) the class of problems for which it tends to be appropriate; (b) how to access it directly via the LINDO API or via various modelling language front ends; (c) heuristics used for finding good initial solutions; (d) methods for constructing easily solved relaxations; (e) branching rules for splitting a problem into more easily solved subproblems; and (f) some illustrative computational results.